What's the difference between Leave-One-Out and K-Fold Cross validation? As far as I know in K-fold cross validation the samples are split into k sets and at round k-1 of these are used for the training of the model and the last one is used for testing the model and estimating the error of the model. Totally k measurements are done and finally is made the mean of the errors.
So, if my description of the k-fold is more or less correct, what's the difference from Leave-One-Out Cross validation?
EDIT: Actually I don't care about the value of k, I simply don't see the difference between LOO and K-fold Cross validation.
 A: In loocv method we divide the dataset as one data point for test data while all the remaining data points as our train data. We then validate our model by using this n-1 train data against 1 test data. We perform n iterations like this with 1 test data being forwarded and remaining n-1 data being our new train data. This is suitable in time series analysis. We then find the average   of n rmse values obtained. While in k fold method we divide the entire dataset in mot k folds and one fold will be the test fold and k-1 fold will be the train fold. We then validate our model by training k-1 train fold against  1 test fold. We do such k iterations and average the k rmse values. The test fold here moves backward and forward. Hence it cannot be used in time series analysis since it messes up with time. Please somebody correct me if am wrong somewhere 
A: Leave-one-out fits the model with k-1 observations and classifies the remaining observation left out.  It differs from your description because this process is repeated another k-1 times with a different observation left out.  You can learn about this from the original paper by Lachenbruch and Mickey in 1968. In my answer I am treating k as the full sample size. In k-fold cross-validation it has a different meaning.
