What is the purpose of cross-validation?

The purpose of cross-validation is primarily to estimate the skill of machine learning models. This technique involves dividing the dataset into subsets, training the model on some subsets while validating it on others. By doing so, cross-validation helps in assessing how well the model generalizes to an independent data set, which is critical for understanding its predictive performance.

Cross-validation provides a more reliable evaluation than simply splitting the data into a training set and a test set once, as it minimizes the variability associated with any single split. This way, the model's performance metrics, such as accuracy or F1-score, can be averaged over multiple iterations, giving a clearer picture of how the model is likely to perform on unseen data.

The other options do not capture the primary intention of cross-validation. While it may incidentally help streamline data management or computational efficiency in certain cases, these are not its main purposes. Similarly, ensuring data security is unrelated to the technique, as cross-validation focuses on model evaluation rather than data protection.