Big O notation provides a framework for analyzing the performance of functions based on how their execution time grows with increasing input size. The article discusses four common categories of Big O notation: constant (O(1)), logarithmic (O(log n)), linear (O(n)), and quadratic (O(n^2)), explaining their implications through examples such as summation, sorting, and searching algorithms. It emphasizes the importance of understanding these complexities to optimize code performance effectively.