The correct answer is: A. To split data into training and testing sets.
Cross-validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It does this by splitting the data into multiple subsets, and then training the model on one subset and evaluating it on another. This process is repeated multiple times, each time with a different subset used for testing. The average performance of the model on the testing sets is then used as an estimate of the model’s performance on unseen data.
Option B is incorrect because cross-validation does not involve training models with different algorithms. Instead, it involves training a single model on multiple subsets of the data.
Option C is incorrect because cross-validation does not involve visualizing data relationships. Instead, it involves splitting the data into multiple subsets and then training and evaluating the model on each subset.
Option D is incorrect because cross-validation does not involve maximizing prediction accuracy. Instead, it involves estimating the model’s performance on unseen data.