What is bias–variance trade-off in Data Science?

Our objective when utilizing Data Science or Machine Learning is to create a model with low bias and variance. We all know that bias and variance are mistakes that emerge when a model is either simple or excessively sophisticated. As a result, while we’re developing a model, we’ll only be able to achieve high accuracy if we understand the tradeoff between bias and variance.
When a model is too simplistic to capture the patterns in a dataset, it is said to be biassed. We need to make our model more complicated to reduce bias. Although increasing the complexity of our model can reduce bias, if we make it too complicated, it may become excessively inflexible, resulting in excessive variance. As a result, the tradeoff between bias and variance is that increasing complexity reduces bias while increasing variance and decreasing complexity increases bias while decreasing variance. Our objective is to establish a balance between a model that is complicated enough to provide minimal bias but not so complicated that it produces significant variation.