In my day-to-day research, a problem I would face quite often is selecting a proper statistical model that fits my data. Mostly, I would be using statistical models for smoothing out erroneous signals from DNA data and I believe it is a common concern among Data Science enthusiasts to pick a model to explain the behavior of data. I thought it would be useful to share how one might tackle such situations using the libraries that are available off-the-shelf. Furthermore, selecting a model is not enough unless you know the most suitable hyperparameters to progress. Let's look at a simple scenario to understand how it can be done.