Supervised learning, which is one of the main type of ML is about learning from examples of input and desired output. In some cases it is easier to define the behavior of a function using such example pairs as opposed to specifying exactly what the learned function ought to do. This usually happens when we aren't certain what the right function value should be at every point in its input space . This is where ML should be used.

There are other functions, for example, searching, sorting etc where its easier to specify accurately what the desired function does compared to giving a list of pairs of examples. In such cases ML may not be the best choice. Note, reasonably accurate sorting functions can be learned from examples, but that's not the most efficient way to design a sorting function.