Either to identify the best model or to understand the nuances of the model with different changes to the data or the hyperparameters -- you would want to perform numerous machine learning experiments. The results could be interesting that enable the process of model selection. As part of my job, I usually have to perform several ML experiments which can be -- (say) to test the effectiveness of dimensionality reduction techniques, text preprocessing techniques (in case of an NLP model), or simple things like playing with the size of the test set. Either way, you might have to run a single code multiple times and record all the observations for comparison later. This is slightly different from the hyperparameter tuning process, and our aim is to identify the technique that best suits our problem.