diff --git a/skll/learner/__init__.py b/skll/learner/__init__.py
index a5202dfe..b265372c 100644
--- a/skll/learner/__init__.py
+++ b/skll/learner/__init__.py
@@ -598,6 +598,8 @@ class Learner(object):
         """
         # create the directory if it doesn't exist
         learner_dir = os.path.dirname(learner_path)
+        if not learner_dir:
+            learner_dir = os.getcwd()
         if not os.path.exists(learner_dir):
             os.makedirs(learner_dir)
         # write out the learner to disk

Saving models in current directory requires `./`
Specifying just a model file name, e.g., `Learner.save(foo.model)` should just assume that we want to save `foo.model` in the current directory. However, it's expecting an explicit directory so we instead need to do `Learner.save('./foo.model')` which is silly. 
diff --git a/tests/test_output.py b/tests/test_output.py
index 1797e580..584f6023 100644
--- a/tests/test_output.py
+++ b/tests/test_output.py
@@ -10,7 +10,6 @@ Tests related to output from run_experiment
 import csv
 import json
 import os
-import platform
 import re
 import warnings
 
@@ -26,14 +25,13 @@ from numpy.testing import (assert_almost_equal,
                            assert_array_equal,
                            assert_array_almost_equal)
 
-from nose.plugins.skip import SkipTest
 from nose.tools import eq_, ok_, assert_raises
 
 from sklearn.datasets import load_digits
 from sklearn.model_selection import ShuffleSplit, learning_curve
 from sklearn.naive_bayes import MultinomialNB
 
-from skll.data import FeatureSet, NDJWriter, Reader
+from skll.data import FeatureSet, NDJReader, NDJWriter, Reader
 from skll.experiments import run_configuration
 from skll.experiments.output import _compute_ylimits_for_featureset
 from skll.learner import Learner
@@ -110,6 +108,9 @@ def tearDown():
                                  'test_send_warnings_to_log*')):
         os.unlink(output_file)
 
+    if exists("test_current_directory.model"):
+        os.unlink("test_current_directory.model")
+
 
 # Generate and write out data for the test that checks summary scores
 def make_summary_data():
@@ -1110,3 +1111,25 @@ def test_send_warnings_to_log():
                        r"ConvergenceWarning:Liblinear failed to converge, "
                        r"increase the number of iterations\.")
         assert convergence_sklearn_warning_re.search(log_content) is not None
+
+
+def test_save_models_to_current_directory():
+    """
+    Test that saving models to current directory works.
+    """
+
+    # create a learner and train it on some data
+    learner1 = Learner('LogisticRegression')
+    train_path = join(_my_dir, 'train', 'f0.jsonlines')
+    train_fs = NDJReader.for_path(train_path).read()
+    learner1.train(train_fs, grid_search=False)
+
+    # save this trained model into the current directory
+    learner1.save("test_current_directory.model")
+
+    # make sure that the model saved and that it's the same model
+    ok_(exists("test_current_directory.model"))
+    learner2 = Learner.from_file("test_current_directory.model")
+    eq_(learner1.model_type, learner2.model_type)
+    eq_(learner1.model_params, learner2.model_params)
+    eq_(learner1.model_kwargs, learner2.model_kwargs)

pip install -e .
parse_log_pytest
requirements.txt
nose nose-cov pytest
apt-get update apt-get install -y gcc
3.7
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
beautifulsoup4==4.13.3
certifi @ file:///croot/certifi_1671487769961/work/certifi
cov-core==1.15.0
coverage==7.2.7
cycler==0.11.0
exceptiongroup==1.2.2
fonttools==4.38.0
importlib-metadata==6.7.0
iniconfig==2.0.0
joblib==1.3.2
kiwisolver==1.4.5
matplotlib==3.5.3
nose==1.3.7
nose-cov==1.6
numpy==1.21.6
packaging==24.0
pandas==1.3.5
Pillow==9.5.0
pluggy==1.2.0
pyparsing==3.1.4
pytest==7.4.4
python-dateutil==2.9.0.post0
pytz==2025.2
ruamel.yaml==0.18.10
ruamel.yaml.clib==0.2.8
scikit-learn==0.22.2.post1
scipy==1.7.3
seaborn==0.12.2
six==1.17.0
-e git+https://github.com/EducationalTestingService/skll.git@bb0353711680746d30d7ef932ad7ea38049c2915#egg=skll
soupsieve==2.4.1
tabulate==0.9.0
tomli==2.0.1
typing_extensions==4.7.1
zipp==3.15.0

name: skll
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2022.12.7=py37h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.4.4=h6a678d5_1
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=22.3.1=py37h06a4308_0
  - python=3.7.16=h7a1cb2a_0
  - readline=8.2=h5eee18b_0
  - setuptools=65.6.3=py37h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.38.4=py37h06a4308_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - beautifulsoup4==4.13.3
      - cov-core==1.15.0
      - coverage==7.2.7
      - cycler==0.11.0
      - exceptiongroup==1.2.2
      - fonttools==4.38.0
      - importlib-metadata==6.7.0
      - iniconfig==2.0.0
      - joblib==1.3.2
      - kiwisolver==1.4.5
      - matplotlib==3.5.3
      - nose==1.3.7
      - nose-cov==1.6
      - numpy==1.21.6
      - packaging==24.0
      - pandas==1.3.5
      - pillow==9.5.0
      - pluggy==1.2.0
      - pyparsing==3.1.4
      - pytest==7.4.4
      - python-dateutil==2.9.0.post0
      - pytz==2025.2
      - ruamel-yaml==0.18.10
      - ruamel-yaml-clib==0.2.8
      - scikit-learn==0.22.2.post1
      - scipy==1.7.3
      - seaborn==0.12.2
      - six==1.17.0
      - soupsieve==2.4.1
      - tabulate==0.9.0
      - tomli==2.0.1
      - typing-extensions==4.7.1
      - zipp==3.15.0
prefix: /opt/conda/envs/skll

tests/test_output.py::test_save_models_to_current_directory
tests/test_output.py::test_multiple_featuresets_and_featurehasher_throws_warning tests/test_output.py::test_learning_curve_output tests/test_output.py::test_learning_curve_output_with_objectives tests/test_output.py::test_learning_curve_plots tests/test_output.py::test_learning_curve_plots_with_objectives tests/test_output.py::test_send_warnings_to_log
tests/test_output.py::test_backward_compatibility tests/test_output.py::test_learning_curve_implementation tests/test_output.py::test_learning_curve_ylimits
skll/learner/__init__.py
skll/learner/__init__.py