Getting to other parameter sets from GridSearchCV in sklearn I am using GridSearchCV to train a dataset with several parameters.  Using the methods on the object I am able to get to the "best" parameter set (based on the scoring function).  I would like to be able to get to one (or more) of the other models.  Is there a way to do that?  Is that saved?  If so what is the way to get to those?

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV

bc = datasets.load_breast_cancer()
X,Y = bc["data"],bc["target"]

sgd = SGDClassifier(loss='log', penalty='l1', max_iter=1E6, tol=1E-6, shuffle=True, random_state=123456)
param_grid = [{'alpha':np.append(10.**-np.arange(3,6), 10.**-np.arange(3,6)/2)}]

sgd_gs = GridSearchCV(sgd, param_grid, scoring='roc_auc', n_jobs=4, cv=4)
sgd_gs.fit(X, Y)
results_pd = pd.DataFrame(sgd_gs.cv_results_).iloc[:,2:7].sort_values(by='rank_test_score').reset_index(drop=True)
results_pd

scores_train = sgd_gs.best_estimator_.predict_proba(X)[:,1]
print sum(scores_train)

#**code below does not seem to work**
params = results_pd.loc[2, 'params']
scores_train = sgd_gs.best_estimator_.set_params(**params).predict_proba(X)[:,1]
print sum(scores_train)
If the parameter refit is set to True, the GridSearchCV object will have the attributes best_estimator_, best_score_ etc. ("Best" measured in terms of the metric provided through the scoring parameter.)

For other models you can only access the cross-validation scores from cv_results_. The models are not stored. If you want to use them to make predictions, you have the re-train the model where you explicitly set the parameters. 

If you want to use all the models to make predictions, it would be better to write your own code and simply loop over the parameter constellations without using GridSearchCV.