Final answer:
In k-fold cross validation, accuracy is a common metric used to compare models.
Step-by-step explanation:
When comparing models using k-fold cross validation, a commonly used metric is the accuracy of the models. Accuracy measures the proportion of correctly classified instances out of the total instances.
Other metrics that can be used include precision, recall, and F1-score. Precision measures the proportion of correctly classified positive instances out of all instances classified as positive. Recall measures the proportion of correctly classified positive instances out of all actual positive instances. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of model performance.