Effect of value of k in K-Nearest Neighbor
There is no simple answer.  The standard approach to choose $k$ is to try different values of $k$ and see which provides the best accuracy on your particular data set (using cross-validation or hold-out sets, i.e., a training-validation-test set split).

Intuitively, $k$-nearest neighbors tries to approximate a locally smooth function; larger values of $k$ provide more "smoothing", which or might not be desirable.