diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
index c13d59a8daa2b..4cde38bfa0179 100644
--- a/sklearn/metrics/scorer.py
+++ b/sklearn/metrics/scorer.py
@@ -19,7 +19,6 @@
 # License: Simplified BSD
 
 from abc import ABCMeta, abstractmethod
-from collections import Iterable
 import warnings
 
 import numpy as np
@@ -27,8 +26,8 @@
 from . import (r2_score, median_absolute_error, mean_absolute_error,
                mean_squared_error, mean_squared_log_error, accuracy_score,
                f1_score, roc_auc_score, average_precision_score,
-               precision_score, recall_score, log_loss, balanced_accuracy_score,
-               explained_variance_score, brier_score_loss)
+               precision_score, recall_score, log_loss,
+               explained_variance_score)
 
 from .cluster import adjusted_rand_score
 from .cluster import homogeneity_score
@@ -136,10 +135,7 @@ def __call__(self, clf, X, y, sample_weight=None):
         """
         super(_ProbaScorer, self).__call__(clf, X, y,
                                            sample_weight=sample_weight)
-        y_type = type_of_target(y)
         y_pred = clf.predict_proba(X)
-        if y_type == "binary":
-            y_pred = y_pred[:, 1]
         if sample_weight is not None:
             return self._sign * self._score_func(y, y_pred,
                                                  sample_weight=sample_weight,
@@ -212,7 +208,6 @@ def __call__(self, clf, X, y, sample_weight=None):
     def _factory_args(self):
         return ", needs_threshold=True"
 
-
 def get_scorer(scoring):
     """Get a scorer from string
 
@@ -236,8 +231,8 @@ def get_scorer(scoring):
             valid = False  # Don't raise here to make the error message elegant
         if not valid:
             raise ValueError('%r is not a valid scoring value. '
-                             'Valid options are %s'
-                             % (scoring, sorted(scorers)))
+                             'For valid options use sorted(SCORERS.keys())'
+                             % (scoring))
     else:
         scorer = scoring
     return scorer
@@ -301,10 +296,6 @@ def check_scoring(estimator, scoring=None, allow_none=False):
                 "If no scoring is specified, the estimator passed should "
                 "have a 'score' method. The estimator %r does not."
                 % estimator)
-    elif isinstance(scoring, Iterable):
-        raise ValueError("For evaluating multiple scores, use "
-                         "sklearn.model_selection.cross_validate instead. "
-                         "{0} was passed.".format(scoring))
     else:
         raise ValueError("scoring value should either be a callable, string or"
                          " None. %r was passed" % scoring)
@@ -505,7 +496,6 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
 # Standard Classification Scores
 accuracy_scorer = make_scorer(accuracy_score)
 f1_scorer = make_scorer(f1_score)
-balanced_accuracy_scorer = make_scorer(balanced_accuracy_score)
 
 # Score functions that need decision values
 roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True,
@@ -523,9 +513,6 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
 log_loss_scorer = make_scorer(log_loss, greater_is_better=False,
                               needs_proba=True)
 log_loss_scorer._deprecation_msg = deprecation_msg
-brier_score_loss_scorer = make_scorer(brier_score_loss,
-                                      greater_is_better=False,
-                                      needs_proba=True)
 
 
 # Clustering scores
@@ -549,11 +536,9 @@ def make_scorer(score_func, greater_is_better=True, needs_proba=False,
                mean_absolute_error=mean_absolute_error_scorer,
                mean_squared_error=mean_squared_error_scorer,
                accuracy=accuracy_scorer, roc_auc=roc_auc_scorer,
-               balanced_accuracy=balanced_accuracy_scorer,
                average_precision=average_precision_scorer,
                log_loss=log_loss_scorer,
                neg_log_loss=neg_log_loss_scorer,
-               brier_score_loss=brier_score_loss_scorer,
                # Cluster metrics that use supervised evaluation
                adjusted_rand_score=adjusted_rand_scorer,
                homogeneity_score=homogeneity_scorer,