sklearn.model_selection

sklearn.model_selection is a module in scikit-learn library which provides various tools for model selection and evaluation. It contains functions for splitting data into train and test sets, cross-validation, and grid search for hyperparameter tuning.

Some of the commonly used functions in sklearn.model_selection are:

1. train_test_split(): Splits data into random train and test subsets
2. KFold(): Splits data into k-folds for cross-validation
3. GridSearchCV(): Performs grid search over specified parameter values for hyperparameter tuning
4. RandomizedSearchCV(): Performs randomized search over specified parameter values for hyperparameter tuning
5. StratifiedKFold(): Splits data into k-folds while preserving the class distribution
6. TimeSeriesSplit(): Splits time-series data into train and test sets while preserving temporal order

Overall, sklearn.model_selection is an important module for developing and evaluating machine learning models.