Why Cross Entropy Loss?

While solving classification problems using deep learning models, we use cross entropy to tell the model how good or bad it's predictions are during training. What is this cross entropy loss? Cross entropy in a way can be looked as the difference between 2 probability distributions in the case of supervised learning with one-hot encoded labels. Let's say we are trying to classify an input between 3 categories. It is okay if you don't understand this next piece of code, this is just to show us the cross entropy value. If we consider the probabilities as 2 vectors and find the squared distance between them, we get the L2 loss.