There are many ways to approach machine learning, and selecting the right algorithm is just the first step. What a model can truly offer in terms of performance can be distilled to how well it is fine-tuned. Here, the analogy is the adjusting of dials on a supercharged engine, which is otherwise called hyperparameters.
Hyperparameter tuning is the act of modifying the parameters of a model — that is, the parameters defining the model’s architecture — to achieve optimal performance. Choose it wisely and your project will achieve optimal efficiency and flexibility. Oppositely, if it’s screwed up, the model may underperform or overlearn.