A way to test a model by taking turns training and checking on data.
Cross-validation is like tasting soup from different spots in the pot. One spoonful may be all noodles and no broth.
It trains and checks the model on different data pieces, then rotates the pieces. You meet it with small datasets and model tuning.
Benchmark contamination
Cross-validation reduces fake high scores from lucky test matches.
HPO
Cross-validation helps compare different parameter settings.
Bias-Variance Tradeoff
Cross-validation helps show if a model overfits or underfits.
Supervised Learning
Cross-validation is a common way to test supervised learning models.