Select this option to compensate for an optimistic apparent error rate of misclassified observations. The apparent error rate is the percentage of misclassified observations. This number tends to be optimistic because the data being classified are the same data used to build the classification function.
With cross validation, Minitab omits each observation one at a time and calculates the discriminant function with the remaining observations. Then Minitab predicts the group for the omitted observation. If the proportion of correct groups is high, then you can have confidence in the predictions.
If you use cross-validation, Minitab displays an additional summary table and adds cross-validation information to the Summary of Misclassified Observations table.
Another way to calculate a more realistic error rate is to split your data into two parts. Use one part to create the discriminant function, and the other part as a validation set. Predict group membership for the validation set and calculate the error rate as the percentage of these data that are misclassified.