What is cross-validation used for in model evaluation?

Cross-validation is a robust technique used in model evaluation to determine how well a predictive model will perform on an independent dataset. The primary goal of cross-validation is to assess the model's ability to generalize beyond the data it was trained on, thereby providing insights into its predictive power and reliability in real-world applications.

The process typically involves dividing the original dataset into multiple subsets or "folds." The model is trained on some of these subsets and validated on the remaining ones. This is repeated several times, with different splits, to ensure that every sample in the dataset gets a chance to be both in the training and validation sets. This iterative approach helps mitigate the risk of overfitting, where a model learns the noise in the training data instead of the actual underlying patterns.

By evaluating the model across different splits of data, cross-validation gives a more accurate estimate of model performance compared to merely testing it on a single train-test split. It captures variations in data and provides a comprehensive view of how the model is likely to perform on unseen data, which is crucial for effective decision-making in many applications, from finance to healthcare.

Thus, the primary use of cross-validation in model evaluation is to rigorously assess how well results generalize to independent datasets, solid