CROSS VALIDATION: It is a statistical technique used in the machine learning and data analysis to find the accuracy of the data and predict the future data which helps for underfitting and overfitting.
leave one out cross validation: In this particular validation one part of the data will be taken as the test and remaining will be the train data set,for suppose if we have the 100 datasets in the first experiment the first dataset will be the tets and remaining 99 datas willl be the train.in the sameway the process will be done for each and every entity.it consumes a lot of time, and when the new dataaset is implemented into this process there are high chance to low accuracy and high errors.
K-Fold cross validation: In this validation the k is taken as the some value and the k value will be divided by the total dataset,then the dataset will be performed k times to get the accurate value,the mean of acurates gives the exact validation data.
Stratified K-Fold cross validation: This validation is similar to k-fold cross validation,here but it ensures that each fold has a similar class distribution to the original dataset. this validation is useful for the imbalanced datasets.
Time series cross validation: In this validation using the current data the future data will be predicted,for example taking the stock price datas,we have the previous 5 days data and we need to predict the next day stock price, here the time series cross validation will be used.