Visualizations examples in documentation don't work

### Describe the issue linked to the documentation

The examples at https://scikit-learn.org/stable/visualizations.html don't work as shown.  Both produce error messages and no graphics.

### Suggest a potential alternative/fix

Modify documentation examples so they work.

COMMENT:
The examples are missing the `load_wine` step.  Loading with `X, y = load_wine(return_X_y=True)` doesn't work either.

```python
$ ipython
Python 3.8.10 (default, May  4 2021, 00:00:00) 
Type 'copyright', 'credits' or 'license' for more information
IPython 8.0.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: from sklearn.model_selection import train_test_split
   ...: from sklearn.svm import SVC
   ...: from sklearn.metrics import RocCurveDisplay
   ...: from sklearn.datasets import load_wine
   ...: 
   ...: X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
   ...: svc = SVC(random_state=42)
   ...: svc.fit(X_train, y_train)
   ...: 
   ...: svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [1], in <module>
      3 from sklearn.metrics import RocCurveDisplay
      4 from sklearn.datasets import load_wine
----> 6 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
      7 svc = SVC(random_state=42)
      8 svc.fit(X_train, y_train)

NameError: name 'X' is not defined

In [2]: import sklearn as sk

In [3]: sk.__version__
Out[3]: '1.0.2'

In [4]: X, y = load_wine(return_X_y=True)

In [5]: X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
   ...: svc = SVC(random_state=42)
   ...: svc.fit(X_train, y_train)
   ...: 
   ...: svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [5], in <module>
      2 svc = SVC(random_state=42)
      3 svc.fit(X_train, y_train)
----> 5 svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)

File ~/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py:231, in RocCurveDisplay.from_estimator(cls, estimator, X, y, sample_weight, drop_intermediate, response_method, pos_label, name, ax, **kwargs)
    227 check_matplotlib_support(f"{cls.__name__}.from_estimator")
    229 name = estimator.__class__.__name__ if name is None else name
--> 231 y_pred, pos_label = _get_response(
    232     X,
    233     estimator,
    234     response_method=response_method,
    235     pos_label=pos_label,
    236 )
    238 return cls.from_predictions(
    239     y_true=y,
    240     y_pred=y_pred,
   (...)
    246     **kwargs,
    247 )

File ~/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py:108, in _get_response(X, estimator, response_method, pos_label)
    106     y_pred_shape = y_pred.shape[1]
    107     if y_pred_shape != 2:
--> 108         raise ValueError(
    109             f"{classification_error} fit on multiclass ({y_pred_shape} classes)"
    110             " data"
    111         )
    112     y_pred = y_pred[:, class_idx]
    113 elif pos_label == estimator.classes_[0]:  # `decision_function`

ValueError: Expected 'estimator' to be a binary classifier, but got SVC fit on multiclass (3 classes) data

In [6]: 
```
```python
In [6]: import matplotlib.pyplot as plt
   ...: from sklearn.ensemble import RandomForestClassifier
   ...: 
   ...: rfc = RandomForestClassifier(random_state=42)
   ...: rfc.fit(X_train, y_train)
   ...: 
   ...: ax = plt.gca()
   ...: rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
   ...: svc_disp.plot(ax=ax, alpha=0.8)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [6], in <module>
      5 rfc.fit(X_train, y_train)
      7 ax = plt.gca()
----> 8 rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
      9 svc_disp.plot(ax=ax, alpha=0.8)

File ~/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py:231, in RocCurveDisplay.from_estimator(cls, estimator, X, y, sample_weight, drop_intermediate, response_method, pos_label, name, ax, **kwargs)
    227 check_matplotlib_support(f"{cls.__name__}.from_estimator")
    229 name = estimator.__class__.__name__ if name is None else name
--> 231 y_pred, pos_label = _get_response(
    232     X,
    233     estimator,
    234     response_method=response_method,
    235     pos_label=pos_label,
    236 )
    238 return cls.from_predictions(
    239     y_true=y,
    240     y_pred=y_pred,
   (...)
    246     **kwargs,
    247 )

File ~/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py:108, in _get_response(X, estimator, response_method, pos_label)
    106     y_pred_shape = y_pred.shape[1]
    107     if y_pred_shape != 2:
--> 108         raise ValueError(
    109             f"{classification_error} fit on multiclass ({y_pred_shape} classes)"
    110             " data"
    111         )
    112     y_pred = y_pred[:, class_idx]
    113 elif pos_label == estimator.classes_[0]:  # `decision_function`

ValueError: Expected 'estimator' to be a binary classifier, but got RandomForestClassifier fit on multiclass (3 classes) data

In [7]: 
```

Labels: Documentation