What is cross-validation used for in model evaluation?

Cross-validation is utilized primarily to assess a model's ability to generalize to unseen data. This technique involves partitioning the training dataset into multiple subsets, allowing the model to be trained on one subset while validating its performance on another. By rotating through these subsets, cross-validation provides a more reliable estimate of model performance compared to simply dividing the dataset into a single training and testing set.

The significance of this process lies in its ability to give insights into how well the model will perform when applied to new, unseen data—essentially testing the model's predictive power and robustness. This is crucial in data science because a model that performs well on training data might not necessarily do well when faced with new input, highlighting the importance of generalization over memorization of the training data.

Other options focus on aspects unrelated to the core purpose of cross-validation. For example, improving data quality and ensuring data security pertain more to data preprocessing and management practices rather than model evaluation. Simplifying the model architecture relates to the design of the model itself rather than how its performance is assessed. Therefore, the correct choice directly aligns with the essential goal of cross-validation in evaluating a model's effectiveness in a real-world setting.