I would like to note that even with algorithms/datastructures "best" is USUALLY not the word (even assuming that all algorithms/structures are discovered/known). "Good enough" / "fits in my multi dimensional budget" is reality:
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It is easy to order two real numbers. 1 is greater than 0.
But can we order points on the cartesian plane (1,2) or (2,1) or (0,100000000000)? Already at 2nd dimension not all points are easily ordered. There is solution "just give priority to 1st coordinate", but can you really completely disregard memory usage and focus solely on cpu?
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Theoretically, to get "best", one needs to evaluate not only O(n) of avg cpu usage, and avg memory usage, but also worst and best cases, while using knowledge of input data distribution (e.g. maybe input is almost sorted). Memory access patterns, cache locality, battery life, suitability for your hardware also must come into play.
(many dimensions)
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Practially one has to do profiling on real hardware with real configurations / inputs / workloads. Different workloads may favor different algorithms/structures.
(again results with many dimensions)
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Due to constrained time people will not even go over all algorithms. Real people will immediatly rule out really bad ones, then pick 1 or 2 algorithms that theoretically are good enough match, and see if their profiling results fit in cpu/memory budgets.
(even if budget is not on paper, but just part of intuition)