What is the primary purpose of cross-validation?

The primary purpose of cross-validation is to assess how statistical results generalize to independent datasets. This technique involves partitioning the original dataset into multiple subsets, or "folds." A model is trained on a portion of the data and tested on another, which helps gauge its performance and ability to predict unseen data.

Cross-validation is crucial in validating the effectiveness of a predictive model by providing a more comprehensive picture of its accuracy and robustness. By testing the model on different subsets of the data, one can identify how well it performs across various conditions, reducing the risk of overfitting, where a model may excel on training data yet fail to perform on new data.

This evaluation process is fundamental in model selection and tuning, ensuring that the final model is not just tailored to the training data, but capable of making accurate predictions in real-world situations.