the previously described process seems to be good we have described the validation part of the process but we haven' touched on the cross part of it so where is the cross-validationto understand that intricacy of the cv processwe would have to discuss why we need this in the first place the need for it arises from the fact that by dividing the data into test and validation set we have lost out on decent amount of data which we could have used to further refine our modeling process also another important point is that if we take model' error for single iteration to be its overall error we are making serious mistake instead we want to take some average measure of error by building multiple iterations of the same model but if we keep rebuilding the model on the same datasetwe are not going to get much difference in the model performance we address these two issues by introducing the cross concept of cross-validation the idea of cross validation is to get different splits of train and validation sets (different observations in each seteach timeusing some strategy (which we will elaborate on laterand then build multiple iterations of each model on these different splits the average error on these splits is then reported as the error of the model in question and the final decision is made on this averaged error metric this strategy has brilliant effect on the estimated error of each modelas it ensures that the averaged error is close approximation of the model' error on really unseen data (here our test setand we could also leverage the complete training dataset for building the model this process is explained pictorially in figure - figure - the final cross-validation process for model building and tuning the various strategies in which these different train and validation sets can be generated gives rise to different kind of cross-validation strategies the common idea in each of these strategies remains the same the only difference is in the way the original train set is split into train and validation set for each iteration of model building ross-validation strategies we explained the basic principle of cross validation in the previous section in this sectionwe see the different strategies in which we can split the training data into training and validation data apart from the way of this splitas mentioned beforethe process for each of these strategies remains the same the major types of cross-validation strategies are described as follows