Explain Cross-Validation concept in Machine Learning?

I think the key points here are the performance and generalization ability of a model. In spirit, just like human learning process, we split our data set into train and test set. Give the model a train set to learn and a test set to test. The model needs to pass its exam to graduate from “training academy”. The problem is that, with this simple process, the model is only trained once. Then, it might not be ready for a difficult test and did not learn enough to be capable of predicting well for any kind of data (generalization ability). This is where k-fold cross validation comes into play. It involves partitioning the dataset into k subsets(folds) randomly, to train and evaluate the model iteratively. Here are the steps to perform cross-validation:  

1.  Divide the dataset into k equal-sized and non-overlapping folds

2. Select one fold as the validation set and the remaining folds as the train set. This process is repeated for each fold, so every fold is used as the validation set exactly once.

3. Train the machine learning model on the train set. Evaluate the trained model on the validation set with certain metrics.

4. Repeat the step 3 for each fold. This ensures that the model is evaluated on different subsets of the dataset and provides a more robust assessment of its performance.

5. Once all the folds have been used as the validation set, calculate the average performance metric across all the folds. This gives an overall estimate of the model’s accuracy and generalization ability.