KFold(n_samples=n) not equivalent to LeaveOneOut() cv in CalibratedClassifierCV()

### Describe the bug

Calling `CalibratedClassifierCV()` with `cv=KFold(n_samples=n)` (where n is the number of samples) can give different results than using `cv=LeaveOneOut()`, but the docs for `LeaveOneOut()` say these should be equivalent. 

In particular, the `KFold` class has an `"n_splits"` attribute, which means [this branch](https://github.com/scikit-learn/scikit-learn/blob/8721245511de2f225ff5f9aa5f5fadce663cd4a3/sklearn/calibration.py#L387) runs when setting up sigmoid calibration, and then [this error](https://github.com/scikit-learn/scikit-learn/blob/8721245511de2f225ff5f9aa5f5fadce663cd4a3/sklearn/calibration.py#L394) can be thrown. With `LeaveOneOut()`, `n_folds` is set to `None` and that error is never hit.

I'm not sure whether that error is correct/desirable in every case (see the code to reproduce for my use case where I think(?) the error may be unnecessary) but, either way, the two different `cv` values seem like they should behave equivalently.

### Steps/Code to Reproduce

```py
from sklearn.pipeline import make_pipeline
from sklearn.calibration import CalibratedClassifierCV
from sklearn.model_selection import KFold, LeaveOneOut
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=20, random_state=42)

pipeline = make_pipeline(
    StandardScaler(),
    CalibratedClassifierCV(
        SVC(probability=False),
        ensemble=False,
        cv=LeaveOneOut()
    )
)
pipeline.fit(X, y)

pipeline2 = make_pipeline(
    StandardScaler(),
    CalibratedClassifierCV(
        SVC(probability=False),
        ensemble=False,
        cv=KFold(n_splits=20, shuffle=True)
    )
)
pipeline2.fit(X, y)
```

### Expected Results

`pipeline` and `pipeline2` should function identically. Instead, `pipeline.fit()` succeeds and `pipeline2.fit()` throws.

### Actual Results

```
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/python3.11/site-packages/sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/python3.11/site-packages/sklearn/pipeline.py", line 427, in fit
    self._final_estimator.fit(Xt, y, **fit_params_last_step)
  File "/python3.11/site-packages/sklearn/base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/python3.11/site-packages/sklearn/calibration.py", line 419, in fit
    raise ValueError(
ValueError: Requesting 20-fold cross-validation but provided less than 20 examples for at least one class.
```

### Versions

```shell
System:
    python: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:34:54) [Clang 16.0.6 ]
   machine: macOS-14.4.1-arm64-arm-64bit

Python dependencies:
      sklearn: 1.3.2
          pip: 24.0
   setuptools: 69.0.2
        numpy: 1.26.2
        scipy: 1.11.4
       Cython: None
       pandas: 2.1.3
   matplotlib: 3.8.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: libomp
        version: None

       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
        version: 0.3.23.dev
threading_layer: pthreads
   architecture: armv8

       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
        version: 0.3.21.dev
threading_layer: pthreads
   architecture: armv8
```
```

COMMENT:
LeaveOneOut does not have different groups like k-folds cv (https://www.cs.cmu.edu/~schneide/tut5/node42.html). More accurately, it sets each sample as a 'fold.' It trains on (n-1) training data at a time (where train data size = n) making it computationally expensive but very reliable. K-folds, on the other hand, divides the training data into k groups and trains the model k times, leaving one group at a time. Perhaps this clarification was not the main issue, but I thought it might be helpful :)

Labels: Bug