The GridSearchCV class in the hana_ml.algorithms.pal.model_selection module is used for exhaustive search over specified parameter values for an estimator with crossover validation, allowing for the exploration of any sequence of parameter settings.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.model_selection import GridSearchCV
from hana_ml.algorithms.pal.unified_classification import UnifiedClassification

# Create an "UnifiedClassification" object
uhgc = UnifiedClassification(func='HybridGradientBoostingTree')

# Create a "GridSearchCV" object
gscv = GridSearchCV(
    estimator=uhgc,
    param_grid={
        'learning_rate': [0.1, 0.4, 0.7, 1],
        'n_estimators': [4, 6, 8, 10],
        'split_threshold': [0.1, 0.4, 0.7, 1]
    },
    train_control={
        'fold_num': 5,
        'resampling_method': 'cv',
        'random_state': 1,
        'ref_metric': ['error_rate']
    },
    scoring='error_rate'
)

# Assuming df_train is your training data
# Invoke fit()
gscv.fit(
    data=df_train,
    key='ID',
    label='CLASS',
    partition_method='stratified',
    partition_random_state=1,
    stratified_column='CLASS'
)

# Predict
# Assuming df_test is your test data
predictions = gscv.predict(data=df_test)
```

Please replace `df_train` and `df_test` with your actual training and test data.