[MRG] Makes roc_auc_score and average_precision_score docstrings more explicit
diff --git a/sklearn/metrics/ranking.py b/sklearn/metrics/ranking.py
index 2003ed8b314c8..fde1f1c441125 100644
--- a/sklearn/metrics/ranking.py
+++ b/sklearn/metrics/ranking.py
@@ -116,7 +116,7 @@ def average_precision_score(y_true, y_score, average="macro",
     Parameters
     ----------
     y_true : array, shape = [n_samples] or [n_samples, n_classes]
-        True binary labels in binary label indicators.
+        True binary labels (either {0, 1} or {-1, 1}).
 
     y_score : array, shape = [n_samples] or [n_samples, n_classes]
         Target scores, can either be probability estimates of the positive
@@ -200,7 +200,7 @@ def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
     Parameters
     ----------
     y_true : array, shape = [n_samples] or [n_samples, n_classes]
-        True binary labels in binary label indicators.
+        True binary labels (either {0, 1} or {-1, 1}).
 
     y_score : array, shape = [n_samples] or [n_samples, n_classes]
         Target scores, can either be probability estimates of the positive
@@ -438,8 +438,8 @@ def roc_curve(y_true, y_score, pos_label=None, sample_weight=None,
     ----------
 
     y_true : array, shape = [n_samples]
-        True binary labels in range {0, 1} or {-1, 1}.  If labels are not
-        binary, pos_label should be explicitly given.
+        True binary labels. If labels are not either {-1, 1} or {0, 1}, then
+        pos_label should be explicitly given.
 
     y_score : array, shape = [n_samples]
         Target scores, can either be probability estimates of the positive

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#### Reference Issue
<!-- Example: Fixes #1234 -->

Fixes issue #9554

#### What does this implement/fix? Explain your changes.

Changes the description of the `y_true` parameter for `metrics.roc_auc_score`
and `metrics.average_precision_score` to be slightly more explicit about 
the allowed values. This is more in line with the `metrics.roc_curve` and 
`metrics.precision_recall_curve` description of `y_true`. Specifically, this PR 
makes the following modification.  

Current description:
`True binary labels in binary label indicators.`

Modified description:
`True binary labels (either {0, 1} or {-1, 1}) or binary label indicators.`

This could help with avoid confusion in situations like the following

```python
from sklearn.metrics import roc_auc_score
import numpy as np

y_true = np.array([-1, -1, 0, 0])
y_scores = np.array([0.1, 0.4, 0.35, 0.8])
roc_auc_score(y_true, y_scores)
```

which yields
 
```python
ValueError: Data is not binary and pos_label is not specified
```

Even though there are only two unique classes in `y_true`. 

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