The computational complexity of K-NN increases as the size of the training data set increase and the algorithm gets significantly slower as the number of examples and independent variables increase.
Also, K-NN is a non-parametric machine learning algorithm and as such makes no assumption about the functional form of the problem at hand.
The algorithm works better with data of the same scale, hence normalizing the data prior to applying the algorithm is recommended.
