Random search grid not displaying scoring metric

I want to do a grid search of some few hyperparameters through a XGBClassifier of a binary class, but whenever i run it the score value (roc_auc) is not being display. I read in other question that this can be related to some error in model training but i am not sure which one is in this case.

My model training data X_train is a np.array of (X, 19)

and my y_train is a numpy.ndarray of shape (X, ) which looks like this

And then i create my model params and model in this way

from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from xgboost import XGBClassifier

# A parameter grid for XGBoost
params = {
        'min_child_weight': [1, 5, 10],
        'gamma': [0.5, 1, 1.5, 2, 5],
        }
xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')

folds = 3
param_comb = 5

skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)

random_search = RandomizedSearchCV(xgb, 
                                   param_distributions=params,
                                   n_iter=param_comb, 
                                   scoring='roc_auc',
                                   n_jobs=4, 
                                   cv=skf.split(X_train, y_train), 
                                   verbose=3, 
                                   random_state=1001)

random_search.fit(X_train, y_train)

Whenever i hit code from above i am seeing this display which doesnt contain the scoring

[CV 3/3] END ..................gamma=0.5, min_child_weight=5; total time= 3.6min
[CV 1/3] END ..................gamma=0.5, min_child_weight=1; total time= 3.7min
[CV 3/3] END .................gamma=0.5, min_child_weight=10; total time= 3.5min
[CV 1/3] END ....................gamma=2, min_child_weight=5; total time= 3.6min
[CV 2/3] END ..................gamma=0.5, min_child_weight=1; total time= 3.5min
[CV 2/3] END .................gamma=0.5, min_child_weight=10; total time= 3.4min
[CV 2/3] END ..................gamma=1.5, min_child_weight=5; total time= 2.5min
[CV 2/3] END ..................gamma=0.5, min_child_weight=5; total time= 3.5min
[CV 2/3] END ....................gamma=2, min_child_weight=5; total time= 3.4min
[CV 3/3] END ..................gamma=0.5, min_child_weight=1; total time= 3.6min
[CV 1/3] END ..................gamma=1.5, min_child_weight=5; total time= 2.5min
[CV 1/3] END ..................gamma=0.5, min_child_weight=5; total time= 3.6min
[CV 3/3] END ....................gamma=2, min_child_weight=5; total time= 3.5min
[CV 1/3] END .................gamma=0.5, min_child_weight=10; total time= 3.4min
[CV 3/3] END ..................gamma=1.5, min_child_weight=5; total time= 2.5min

Answer

The results of the evaluations for each fold are stored in the RandomizedSearchCV instance after fitting. Access them using the cv_results_ attribute:

# Access CV validation scores.
print(random_search.cv_results_)

Note that you can also access the best estimator with:

best_estimator = random_search.best_estimator_

More in the documentation of RandomizedSearchCV.