What is the purpose of cross-validation in data analytics?

The purpose of cross-validation in data analytics is primarily to estimate the model's performance on unseen data. This technique involves partitioning the data into subsets, using some subsets for training the model and others for testing it. By doing so, cross-validation allows analysts and data scientists to gain a better understanding of how well a model will generalize to new, previously unseen data.

This estimation process is crucial for assessing a model's reliability and robustness because it effectively simulates the model's performance in real-world applications where the model will encounter new data. The use of multiple iterations with different data splits provides a comprehensive view of the model's predictive capabilities, reducing the likelihood of overfitting to a single training dataset.

In contrast, other options focus on different aspects of data analytics: increasing the size of training data pertains to techniques like augmentation or sampling; analyzing data for bias involves fairness assessment and ethical considerations; and visualizing data distributions relates to exploratory data analysis, which helps in understanding data characteristics rather than validating model performance.