When looking at data, you’ll want to understand what the underlying relationships are. To do this, you create a model that describes them mathematically. The problem is that a more complex model will fit your initial data better than a simple one. However, they tend to be very brittle: They work well for the data you already have, but try too hard to explain random variations. Therefore, as soon as you add more data, they break down. Simpler models are usually more robust and better at predicting future trends.
Understanding overfitting: an inaccurate meme in Machine Learning How good is your fit? - Ep. 21 (Deep Learning Simplified) Clever Methods of Overfitting Overfitting in Machine Learning: What It Is and How to Prevent It