Feature scaling can vary your results a lot while using certain algorithms and have a minimal or no effect in others.
Most of the times, your dataset will contain features highly varying in magnitudes, units and range. But since, most of the machine learning algorithms use Eucledian distance between two data points in their computations, this is a problem.
If left alone, these algorithms only take in the magnitude of features neglecting the units. The results would vary greatly between different units, 5kg and 5000gms. The features with high magnitudes will weigh in a lot more in the distance calculations than features with low magnitudes.
To suppress this effect, we need to bring all features to the same level of magnitudes. This can be achieved by scaling.
for more info you can check this : https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e
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