c116.txt

Increase the training speed as all data had been converted into the 
normalized range (0,1). All parameters are not in the divergent range.

Balance the importance of each feature. For example, if you have two 
independent variables in different ranges [1, 2] and [1000, 2000] and 
you didn’t scale, then it’s likely the latter variables are considered 
dominent


StandardScaler comes into play when the characteristics of the input 
dataset differ greatly between their ranges, or simply when they are
measured in different units of measure.

The idea behind the StandardScaler is that variables that are measured
 at different scales do not contribute equally to the fit of the model
 and the learning function of the model and could end up creating a 
bias. 

So, to deal with this potential problem, we need to standardize the 
data (μ = 0, σ = 1) that is typically used before we integrate it 
into the machine learning model.