Most of the times, our 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 a feature in the dataset is big in scale(Salary) compared to other(Age) then in algorithms where Euclidean distance is measured this big scaled feature becomes dominating and needs to be normalized.

One more reason is saturation, like in the case of sigmoid activation in Neural Network, scaling would help not to saturate too fast.