K-nearest neighbors is a non-parametric machine learning model in which the model memorizes the training observation for classifying the unseen test data. It can also be called instance-based learning. This model is often termed as lazy learning, as it does not learn anything during the training phase like regression, random forest, and so on. Instead, it starts working only during the testing/evaluation phase to compare the given test observations with the nearest training observations, which will take significant time in comparing each test data point. Hence, this technique is not efficient on big data; also, performance does deteriorate when the number of variables is high due to the curse of dimensionality.