XGBoost

import xgboost as xgb
from sklearn import metrics

def auc(m, train, test): 
    return (metrics.roc_auc_score(y_train,m.predict_proba(train)[:,1]),
                            metrics.roc_auc_score(y_test,m.predict_proba(test)[:,1]))

# Parameter Tuning
model = xgb.XGBClassifier()
param_dist = {"max_depth": [10,30,50],
              "min_child_weight" : [1,3,6],
              "n_estimators": [200],
              "learning_rate": [0.05, 0.1,0.16],}
grid_search = GridSearchCV(model, param_grid=param_dist, cv = 3, 
                                   verbose=10, n_jobs=-1)
grid_search.fit(train, y_train)

grid_search.best_estimator_

model = xgb.XGBClassifier(max_depth=50, min_child_weight=1,  n_estimators=200,\
                          n_jobs=-1 , verbose=1,learning_rate=0.16)
model.fit(train,y_train)

auc(model, train, test)

Great example of a grid search with custom metric.

Generally, you can use the grid_search.best_estimator_ property to access a fit model directly. No need to re-train a model.