diff --git a/rdagent/scenarios/kaggle/experiment/meta_tpl/cross_validation_tpl.py b/rdagent/scenarios/kaggle/experiment/meta_tpl/cross_validation_tpl.py
new file mode 100644
index 00000000..528931d8
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+++ b/rdagent/scenarios/kaggle/experiment/meta_tpl/cross_validation_tpl.py
@@ -0,0 +1,89 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+from sklearn.metrics import accuracy_score, matthews_corrcoef
+from sklearn.model_selection import KFold
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+
+from rdagent.scenarios.kaggle.experiment.meta_tpl.fea_share_preprocess import preprocess
+
+
+def compute_metrics_for_classification(y_true, y_pred):
+    """Compute MCC for classification."""
+    from sklearn.metrics import matthews_corrcoef
+
+    return matthews_corrcoef(y_true, y_pred)
+
+
+def perform_kfold_cross_validation(X, y, n_splits=2, random_seed=42):
+    kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_seed)
+    fold_metrics = []
+
+    DIRNAME = Path(__file__).absolute().resolve().parent
+
+    for fold, (train_idx, valid_idx) in enumerate(kf.split(X)):
+        X_train_fold, X_valid_fold = X.iloc[train_idx], X.iloc[valid_idx]
+        y_train_fold, y_valid_fold = y[train_idx], y[valid_idx]
+
+        # TODO: Preprocess and Feature Engineering before K-Fold CV
+
+        # Preprocess the data
+        X_train_fold = preprocess(X_train_fold)
+        X_valid_fold = preprocess(X_valid_fold)
+
+        # Feature Engineering
+        X_train_l_fold, X_valid_l_fold = [], []
+        for f in DIRNAME.glob("feat*.py"):
+            m = __import__(f.name.strip(".py"))
+            X_train_fold = m.feat_eng(X_train_fold)
+            X_valid_fold = m.feat_eng(X_valid_fold)
+
+            X_train_l_fold.append(X_train_fold)
+            X_valid_l_fold.append(X_valid_fold)
+
+        X_train_fold = pd.concat(X_train_l_fold, axis=1)
+        X_valid_fold = pd.concat(X_valid_l_fold, axis=1)
+
+        # Align features
+        X_valid_fold = X_valid_fold.reindex(columns=X_train_fold.columns, fill_value=0)
+
+        # Train and evaluate models
+        mcc_scores = []
+        model_l = []  # Reinitialize model list
+        for f in DIRNAME.glob("model*.py"):
+            m = __import__(f.name.strip(".py"))
+            model = m.fit(X_train_fold, y_train_fold, X_valid_fold, y_valid_fold)
+            y_valid_pred = m.predict(model, X_valid_fold)
+            mcc = compute_metrics_for_classification(y_valid_fold, y_valid_pred)
+            mcc_scores.append(mcc)
+            print(f"Fold {fold+1}, Model {f.name}: MCC = {mcc}")
+
+        # Store the average MCC score for this fold
+        avg_mcc = np.mean(mcc_scores)
+        fold_metrics.append(avg_mcc)
+        print(f"Fold {fold+1} average MCC: {avg_mcc}")
+
+    # Calculate the overall average MCC
+    overall_avg_mcc = np.mean(fold_metrics)
+    result_df = pd.DataFrame({"Overall Average MCC": [overall_avg_mcc]})
+    result_df.to_csv(f"path/to/playground-series-s4e8/cv_score_{f.name.strip('.py')}.csv", index=False)
+
+    print(f"Overall Average MCC across all folds: {overall_avg_mcc}")
+    return overall_avg_mcc
+
+
+# This allows the script to be run directly
+if __name__ == "__main__":
+    # Load and preprocess the data
+    data_df = pd.read_csv("path/to/playground-series-s4e8/train.csv")
+    data_df = data_df.drop(["id"], axis=1)
+
+    X = data_df.drop(["class"], axis=1)
+    y = data_df[["class"]]
+
+    label_encoder = LabelEncoder()
+    # transfrom y to 1D
+    y = label_encoder.fit_transform(y)
+    result = perform_kfold_cross_validation(X, y)