docs: def roc_auc_cv(self,features,labels):
          """returns an roc auc score depending on the underlying estimator."""        
          if callable(getattr(self.ml, "decision_function", None)):
              return np.mean([self.scoring_function(labels[test],
                              self.pipeline.fit(features[train],labels[train]).
                                           decision_function(features[test]))
                              for train, test in KFold().split(features, labels)])
          elif callable(getattr(self.ml, "predict_proba", None)):
              return np.mean([self.scoring_function(labels[test],
                              self.pipeline.fit(features[train],labels[train]).
                                              predict_proba(features[test])[:,1])
                              for train, test in KFold().split(features, labels)]) 
          else:
              raise ValueError("ROC AUC score won't work with " + self.ml_type + ". No "
                      "decision_function or predict_proba method found for this learner.")