Big O, Theta, and Omega notations all ultimately denote sets of functions. So e.g. O(n) is the set of all functions which are eventually overtaken by some linear function.
When we say "X is O(f(n))" what we really mean is "the function that characterizes X is contained within the set of functions denoted by O(f(n))." Anything that can be described by a function hence can be given Big O, Theta, or Omega notation. So yes worst case time complexity could be described in this notation, but so could average time complexity.
But we could also describe things that have nothing to do with time or space complexity with big O notation as well. As long as we've turned it into a function, we can describe it with big O notation.
When we say "X is O(f(n))" what we really mean is "the function that characterizes X is contained within the set of functions denoted by O(f(n))." Anything that can be described by a function hence can be given Big O, Theta, or Omega notation. So yes worst case time complexity could be described in this notation, but so could average time complexity.
But we could also describe things that have nothing to do with time or space complexity with big O notation as well. As long as we've turned it into a function, we can describe it with big O notation.