Standardization centers each variable on a mean of 0 and scales it to a standard deviation of 1: z = (x - mean) / standard deviation. It puts variables on the same scale, useful for models sensitive to distances or regularization. The mean and standard deviation come from the training set.
Example
from sklearn.preprocessing import StandardScaler
X_s = StandardScaler().fit_transform(X_train)