Validation techniques beyond K-fold

A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning the model's hyperparameters. The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models. There is much confusion in applied machine learning about what a validation dataset is exactly and how it differs from a test dataset. Validation techniques in machine learning are used to get the error rate of the ML model, which can be considered as close to the true error rate of the population. If the data volume is large enough to be representative of the population, you may not need the validation techniques.