Mean Integrated Squared Error (MISE) is a measure used to evaluate the accuracy of an estimator by averaging the squared differences between the estimated and true functions over a domain. It is particularly useful in non-parametric statistics, where it helps in assessing the performance of estimators like kernel density estimators and regression function estimators.