Society of Actuaries PA Practice Exam Study Guide

The chosen answer highlights a significant drawback of cross-validation: its computational expense. Cross-validation involves dividing the dataset into multiple subsets, training the model on some of these subsets while validating it on the others. This process is typically repeated several times, leading to an increase in the overall computational load, especially with larger datasets or more complex models. As a result, the total time and resources required can become substantial, which can be a barrier when quick model training is necessary or when resources are limited.

The other options do not accurately represent disadvantages associated with cross-validation. For instance, it does not simplify the model training process; instead, it adds a layer of complexity by requiring multiple training and validation cycles. It also does not require fewer data samples; rather, it often works best with larger datasets to ensure reliable validation results. Lastly, while cross-validation helps in assessing the model's performance and may reduce the risk of overfitting, it does not guarantee that overfitting will not occur. Therefore, the characteristics of cross-validation can indeed lead to its being computationally expensive, making this choice appropriate.