Cross validation is one of the most trusted techniques for assessing how well a machine learning model will perform on unseen data. Many models appear accurate during training, yet fail when applied to real situations because their evaluation is incomplete. Cross validation helps reduce this gap by offering a more dependable view of model performance,…