How to Check if a Classification Model is Overfitted using scikit-learn

One of the hardest problems, when dealing with Machine Learning algorithms, is evaluating whether the trained model performs well with unseen samples. For example, it may happen that a model behaves very well with a given dataset, but it is not able to predict the correct values, when deployed. This discordance between the trained and testing data can be due to different problems. One of the most common problems is overfitting. A model thats fits the training set well but testing set poorly is said to be overfit to the training set and a model that fits both sets poorly is said to be underfit.