K fold cross validation is a robust technique in machine learning and statistical modeling used to assess the performance of predictive models. The primary purpose is to evaluate the generalizability of a model, ensuring it performs well on unseen data. The dataset is divided into 'k' subsets or 'folds'. The model is trained on 'k-1' folds and tested on the remaining fold. This process is repeated 'k' times, with each fold serving as the test set exactly once.
