Big O notation describes how algorithm performance scales with input size. The guide covers common complexities like O(1) constant time, O(n) linear time, O(n²) quadratic time, and O(log n) logarithmic time with practical JavaScript examples. It demonstrates optimization techniques, common pitfalls like hidden nested loops, and includes performance testing methods. Key takeaway: understanding Big O helps make informed decisions about code efficiency as applications scale.