Answer: To assess the model's performance across various data subsets

The primary goal of employing k-fold cross-validation in Spark ML model evaluation is to gauge the model's effectiveness by utilizing different segments of the dataset. This technique involves partitioning the dataset into k equal parts. The model undergoes training k times, with each iteration using k-1 parts for training and the remaining part for validation. This cycle ensures every part serves as the validation set once, offering a comprehensive view of the model's performance across diverse data configurations. Such an approach minimizes the influence of variability from a single train-test split, yielding a more accurate estimate of the model's generalization capability. K-fold cross-validation is a widely adopted method in machine learning, including Spark ML applications, for model assessment and selection.

Author: LeetQuiz Editorial Team