What we see in here 42:

An alternative approach to Z-score normalization (or standardization) is the so-called  Min-Max scaling (often also simply called “normalization” - a common cause for ambiguities).
In this approach, the data is scaled to a fixed range - usually 0 to 1.
The cost of having this bounded range - in contrast to standardization - is that we will end up with smaller standard deviations, which can suppress the effect of outliers.

Can someone explain the selected part?