is_sequence is too aggressive for determining how to print
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

class Foo(object):
    def __len__(self):
        return 3

    def __iter__(self):
        return iter([1, 2, 3])


class Bar(Foo):
    def __repr__(self):
        return "Hello world"

print(pd.DataFrame([[Foo()],
                    [Bar()]]))
```
Expected output:
```
                                      0
0  <__main__.Foo object at 0x1045b79e8>
1                           Hello world
```
Actual output:
```
           0
0  (1, 2, 3)
1  (1, 2, 3)
```



#### Problem description

`pandas.core.dtypes.inference.is_sequence` appears designed to determine whether an object should be converted directly into a Series, etc. But `is_sequence` is also applied to determine how to `pprint_thing`: if an object in a cell is capable of `len` and `iter` and is not a string-like, [then it is pprinted as a sequence](https://github.com/pandas-dev/pandas/blob/ffae158749c9b72720a7fe3295d323082a5fe420/pandas/io/formats/printing.py#L213-L218). This means the user cannot see the type of the sequence, even when a custom `__repr__` or `__str__` is defined.

This surprised me in the context of putting [`sklearn.model_selection.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)'s `cv_results_` into a DataFrame and displaying it in Jupyter. If one of the parameters is an ensemble estimator, the elements of the ensemble are displayed, obscuring the fact that the parameter is actually a [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) or [RandomForestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) instance, for example.

I think appropriate behaviour would be to use a more strict version of `is_sequence` in `pprint_thing`, which has custom printing only for standard types and their subclasses that do not override `__repr__`.

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8

pandas: 0.23.4
pytest: 3.4.0
pip: 18.0
setuptools: 37.0.0
Cython: 0.27.3
numpy: 1.14.1
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: 1.6.2
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.5
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
