class sklearn.model_selection.StratifiedKFold(n_splits=3, shuffle=False, random_state=None)

sklearn.model_selection.fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, error_score='raise', **fit_params)

class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None

sklearn.model_selection.validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None,

sklearn.model_selection.permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1

class sklearn.model_selection.LeavePOut(p)

sklearn.model_selection.learning_curve(estimator, X, y, groups=None, train_sizes=array([ 0.1, 0.33, 0.55, 0.78, 1. ]), cv=None

class sklearn.model_selection.PredefinedSplit(test_fold)

sklearn.model_selection.cross_val_predict(estimator, X, y=None, groups=None, cv=None, n_jobs=1, verbose=0, fit_params=None

class sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.1, train_size=None, random_state=None)