Discrimination vs Calibration - Machine Learning Models
Discrimination is the separation of the classes while calibration gives us scores based on risk of the population.

For example, there are 100 people that we’d like to predict a disease for and we know that only 3 out of 100 people have this disease. We get their probabilities from our model. Due to good predictability power, our model predicts probabilities between 0-0.05 for 70 people and 0.95-1 for 30 people. This is a good discrimination between classes. We now know that 30 people are at high risk considering only discrimination. But we also know that only 3 out of 100 people get the condition which is 3% prevalence. We use the 3% prevalence to calibrate our scores which will give the actual risk based on population of 100. That means, 0.95 x 0.03 = 0.0285 is their actual risk to the disease.

This is a very crude approach, there are advanced techniques like Kernels, Platt Scaling etc.,