Array API support for cross_validation and friends

Now that #28407 was merged, we need to adopt other cross-validation and model selection tools, starting with `cross_validate`. Currently it fails with:


```python
import array_api_strict as xp

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_validate
from sklearn import set_config
set_config(array_api_dispatch=True)

X, y = make_classification()
cross_validate(LinearDiscriminantAnalysis(), xp.asarray(X), xp.asarray(y))
```

```python-traceback
/Users/ogrisel/code/scikit-learn/sklearn/utils/validation.py:109: UserWarning: You are comparing a array_api_strict dtype against a NumPy native dtype object, but you probably don't want to do this. array_api_strict dtype objects compare unequal to their NumPy equivalents. Such cross-library comparison is not supported by the standard.
  if X.dtype == np.dtype("object") and not allow_nan:
/Users/ogrisel/miniforge3/envs/dev/lib/python3.11/site-packages/array_api_strict/_indexing_functions.py:16: UserWarning: You are comparing a array_api_strict dtype against a NumPy native dtype object, but you probably don't want to do this. array_api_strict dtype objects compare unequal to their NumPy equivalents. Such cross-library comparison is not supported by the standard.
  if indices.dtype not in _integer_dtypes:
Traceback (most recent call last):
  Cell In[14], line 10
    cross_validate(LinearDiscriminantAnalysis(), xp.asarray(X), xp.asarray(y))
  File ~/code/scikit-learn/sklearn/utils/_param_validation.py:213 in wrapper
    return func(*args, **kwargs)
  File ~/code/scikit-learn/sklearn/model_selection/_validation.py:423 in cross_validate
    results = parallel(
  File ~/code/scikit-learn/sklearn/utils/parallel.py:67 in __call__
    return super().__call__(iterable_with_config)
  File ~/miniforge3/envs/dev/lib/python3.11/site-packages/joblib/parallel.py:1863 in __call__
    return output if self.return_generator else list(output)
  File ~/miniforge3/envs/dev/lib/python3.11/site-packages/joblib/parallel.py:1792 in _get_sequential_output
    res = func(*args, **kwargs)
  File ~/code/scikit-learn/sklearn/utils/parallel.py:129 in __call__
    return self.function(*args, **kwargs)
  File ~/code/scikit-learn/sklearn/model_selection/_validation.py:880 in _fit_and_score
    X_train, y_train = _safe_split(estimator, X, y, train)
  File ~/code/scikit-learn/sklearn/utils/metaestimators.py:158 in _safe_split
    X_subset = _safe_indexing(X, indices)
  File ~/code/scikit-learn/sklearn/utils/_indexing.py:264 in _safe_indexing
    return _array_indexing(X, indices, indices_dtype, axis=axis)
  File ~/code/scikit-learn/sklearn/utils/_indexing.py:27 in _array_indexing
    return xp.take(array, key, axis=axis)
  File ~/miniforge3/envs/dev/lib/python3.11/site-packages/array_api_strict/_indexing_functions.py:17 in take
    raise TypeError("Only integer dtypes are allowed in indexing")
TypeError: Only integer dtypes are allowed in indexing
```

Extra notes:

- currently the error message we get when passing Array API inputs to functions or methods that have not been updated to support the Array API are very low level and non informative.
- the `UserWarning` raised at `sklearn/utils/validation.py:109` is not specific to cross-validation. It is also raised when calling `LinearDiscriminantAnalysis().fit(xp.asarray(X), xp.asarray(y))` directly.

COMMENT:
This was fixed when fixing #27096. Some specific CV splitters might still need to be updated but at least it works with the default:

```python
In [1]: import array_api_strict as xp
   ...: 
   ...: from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
   ...: from sklearn.datasets import make_classification
   ...: from sklearn.model_selection import cross_validate
   ...: from sklearn import set_config
   ...: set_config(array_api_dispatch=True)
   ...: 
   ...: X, y = make_classification()
   ...: cross_validate(LinearDiscriminantAnalysis(), xp.asarray(X), xp.asarray(y))


Out[1]: 
{'fit_time': array([0.00247383, 0.00134611, 0.00129104, 0.00127912, 0.00123072]),
 'score_time': array([0.00051308, 0.00046682, 0.00046301, 0.00045109, 0.00044012]),
 'test_score': array([0.95, 0.9 , 0.95, 0.85, 1.  ])}
```

Labels: New Feature, Array API