XGBoost ROC AUC during training does not fit to the end result

I am training a BDT using XGBoost to do binary classification on 22 features. I have 18 Million Samples. (60% for training, 40% for testing)

The ROC AUC I get during training does not fit the end result I get, and I do not understand how this can be. Also, the ROC AUC shows way more overtraining than any other metric, and it appears to have a maximum on the test data.

Has anyone encountered a similar problem before, or has any idea what’s wrong with my model, or how I can find out what’s wrong?

The essence of my code:

params = {
 "model_params": {
    "n_estimators": 2000,
    "max_depth": 4,
    "learning_rate": 0.1,
    "scale_pos_weight": 11.986832275943744,
    "objective": "binary:logistic",
    "tree_method": "hist"
  "train_params": {
    "eval_metric": [

model = xgb.XGBClassifier(**params["model_params"], use_label_encoder=False)
model.fit(X_train, y_train, 
          eval_set=[(X_train, y_train), (X_test, y_test)], 

train_history = model.evals_result()


plt.plot(iterations, train_history["validation_0"]["auc"], label="training data")
plt.plot(iterations, train_history["validation_1"]["auc"], label="test data")


y_pred_proba_train = model.predict_proba(X_train)
y_pred_proba_test = model.predict_proba(X_test)

fpr_test, tpr_test, _ = sklearn.metrics.roc_curve(y_test, y_pred_proba_test[:, 1])
fpr_train, tpr_train, _ = sklearn.metrics.roc_curve(y_train, y_pred_proba_train[:, 1])

auc_test = sklearn.metrics.auc(fpr_test, tpr_test)
auc_train = sklearn.metrics.auc(fpr_train, tpr_train)


plt.title(f"ROC curve, AUC=(test: {auc_test:.4f}, train: {auc_train:.4f})")
plt.plot(fpr_test, tpr_test, label="test data")
plt.plot(fpr_train, tpr_train, label="train data")