O(n) indicates linear time complexity where the execution time increases proportionally with the input size, while O(n^2) indicates quadratic time complexity where the execution time grows with the square of the input size. For example, a simple loop iterating through an array has O(n) complexity, whereas a nested loop that compares every element to every other element has O(n^2) complexity.