What Do I Choose?

Normalization is a good option when you don't know the distribution of your data. It is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks.

Standardization on the other hand is a good option when you have assumed your data has a Gaussian distribution. Although not strictly necessary, this technique is more effective with a Gaussian distribution. It is useful when your data has varying scales and the algorithm you are using does make assumptions about the distribution, such as linear regression, logistic regression, and linear discriminant analysis.