pipe_sgd = Pipeline([('scl', StandardScaler()),
                    ('clf', SGDClassifier(n_jobs=-1))])

param_dist_sgd = {'clf__loss': ['log'],
                 'clf__penalty': [None, 'l1', 'l2', 'elasticnet'],
                 'clf__alpha': np.linspace(0.15, 0.35),
                 'clf__n_iter': [3, 5, 7]}

sgd_randomized_pipe = RandomizedSearchCV(estimator = pipe_sgd, 
                                         param_distributions=param_dist_sgd, 
                                         cv=3, n_iter=30, n_jobs=-1)

sgd_randomized_pipe.fit(X_train, y_train)


#use
scaler = sgd_randomized_pipe.best_estimator_.named_steps['scl']
classifier = sgd_randomized_pipe.best_estimator_.named_steps['clf']