Inconsistency between GridSearchCV 'roc_auc' scoring and roc_auc_score metric

### Describe the bug

I am using GridSearchCV and obtaining different ROC_AUC scores. From my understanding of the documentation and source code, passing 'roc_auc' as a scorer uses the metric.roc_auc_score function. However, passing in the metric scorer and the string result in two different scores (0.833 and 1.0).

I have replicated the metric score by manually doing the gridCV steps including cross-validation and classifying.

### Steps/Code to Reproduce

```
from sklearn.datasets import make_classification
from sklearn.metrics import roc_auc_score, make_scorer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV,StratifiedKFold
from sklearn.pipeline import Pipeline

X,y = make_classification()

lScorers = {'AUC_func':make_scorer(roc_auc_score),'AUC_str':'roc_auc','accuracy':'accuracy'}

pipe = Pipeline(steps=[('clf', None)])
params = [{'clf': [RandomForestClassifier()],'clf__n_estimators': [10],'clf__max_depth': [3],'clf__random_state': [3]}]
clf = GridSearchCV(pipe,param_grid=params,scoring=lScorers, refit='accuracy', n_jobs=-1, cv= StratifiedKFold(3))
clf.fit(X,y)

print(f"AUC From makescorer(roc_auc_score): {clf.cv_results_['mean_test_AUC_func'][0]: .4f}")
print(f"AUC From string roc_auc: {clf.cv_results_['mean_test_AUC_str'][0]:.4f}")
```

### Expected Results

Both AUCs should be the same as they are both using the same scoring function.

### Actual Results

![Screen Shot 2022-11-12 at 5 49 57 PM](https://user-images.githubusercontent.com/10987493/201497580-d7656dbf-e9c0-4dfe-aa8c-07ae6ecfbafa.jpg)


### Versions

```shell
System:
    python: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0]
executable: /root/anaconda3/envs/py10/bin/python
   machine: Linux-5.15.35-1-pve-x86_64-with-glibc2.35

Python dependencies:
      sklearn: 1.1.2
          pip: 22.2.2
   setuptools: 65.5.0
        numpy: 1.22.3
        scipy: 1.7.3
       Cython: None
       pandas: 1.4.4
   matplotlib: 3.5.3
       joblib: 1.1.1
threadpoolctl: 2.2.0

Built with OpenMP: True

threadpoolctl info:
       filepath: /root/anaconda3/envs/py10/lib/libmkl_rt.so.1
         prefix: libmkl_rt
       user_api: blas
   internal_api: mkl
        version: 2021.4-Product
    num_threads: 24
threading_layer: intel

       filepath: /root/anaconda3/envs/py10/lib/libiomp5.so
         prefix: libiomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 28

       filepath: /root/anaconda3/envs/py10/lib/libgomp.so.1.0.0
         prefix: libgomp
       user_api: openmp
   internal_api: openmp
        version: None
    num_threads: 28
```

COMMENT:
```python
In [5]: from sklearn.datasets import make_classification
   ...: from sklearn.metrics import roc_auc_score, make_scorer
   ...: from sklearn.ensemble import RandomForestClassifier
   ...: from sklearn.model_selection import GridSearchCV,StratifiedKFold
   ...: from sklearn.pipeline import Pipeline
   ...: 
   ...: X,y = make_classification(random_state=0)
   ...: 
   ...: lScorers = {'AUC_func':make_scorer(roc_auc_score, needs_threshold=True),'AUC_str':'roc_auc','accuracy':'accuracy'}
   ...: 
   ...: pipe = Pipeline(steps=[('clf', None)])
   ...: params = [{'clf': [RandomForestClassifier(random_state=0)],'clf__n_estimators': [10],'clf__max_depth': [3]}]
   ...: clf = GridSearchCV(pipe,param_grid=params,scoring=lScorers, refit='accuracy', n_jobs=1, cv= StratifiedKFold(3))
   ...: clf.fit(X,y)
   ...: 
   ...: print(f"AUC From makescorer(roc_auc_score): {clf.cv_results_['mean_test_AUC_func'][0]: .4f}")
   ...: print(f"AUC From string roc_auc: {clf.cv_results_['mean_test_AUC_str'][0]:.4f}")
AUC From makescorer(roc_auc_score):  0.9090
AUC From string roc_auc: 0.9090
```

Labels: Bug, Needs Triage