What is the purpose of cross-validation in machine learning?

Cross-validation is a technique used in machine learning to assess how the results of a statistical analysis will generalize to an independent dataset. The primary purpose is to evaluate a model's performance by dividing the data into several subsets or "folds." During this process, the model is trained on a portion of the data while being tested on the remaining folds multiple times. This allows for a more reliable estimation of the model's accuracy and robustness.

By testing the model on different data subsets, cross-validation helps ensure that the model's performance is not excessively dependent on a particular training set, which enhances its stability and reliability. This method reduces the risk of overfitting, where the model performs well on the training data but fails to generalize to unseen data, thereby providing a better understanding of how the model will perform in practice.