Implementation:Scikit learn Scikit learn ModelSelectionModule

Knowledge Sources	Scikit_learn Scikit-learn Docs
Domains	Machine Learning, Model Selection
Last Updated	2026-02-08 15:00 GMT

Overview

Concrete tool for providing cross validation, hyperparameter tuning, and model selection utilities, provided by scikit-learn.

Description

The sklearn.model_selection module aggregates tools for model selection including cross-validation splitters (KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit, LeaveOneOut, ShuffleSplit), hyperparameter search (GridSearchCV, RandomizedSearchCV), validation utilities (cross_val_score, cross_validate, learning_curve, validation_curve), train/test splitting, and threshold classifiers (FixedThresholdClassifier, TunedThresholdClassifierCV).

Usage

Use this module for splitting data into train/test sets, performing cross-validation, tuning hyperparameters, and evaluating model performance across different configurations.

Code Reference

Source Location

• Repository: scikit-learn
• File: sklearn/model_selection/__init__.py

Signature

# Module-level imports (selected):
from sklearn.model_selection._split import KFold, StratifiedKFold, train_test_split
from sklearn.model_selection._search import GridSearchCV, RandomizedSearchCV
from sklearn.model_selection._validation import cross_val_score, cross_validate

Import

from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score

I/O Contract

Inputs

Name	Type	Required	Description
(varies)	N/A	N/A	Each function/class has its own parameters; see individual documentation

Outputs

Name	Type	Description
splits	tuple or iterator	Train/test index arrays from splitters
scores	ndarray	Cross-validation scores from cross_val_score
best_estimator_	estimator	Best found estimator from GridSearchCV/RandomizedSearchCV

Usage Examples

Basic Usage

from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.datasets import load_iris
from sklearn.svm import SVC

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

clf = SVC(kernel="linear")
scores = cross_val_score(clf, X, y, cv=5)
print(f"Accuracy: {scores.mean():.2f} (+/- {scores.std():.2f})")

Related Pages

• Principle:Scikit_learn_Scikit_learn_Grid_Search