Why Is The Maximum Likelihood Estimation (MLE) Method Taken as the Benchmark for Comparing with Other Methods? In many research articles the performance of an estimation method is compared to that of the ML estimation performance. If the performance of the method does not achieve the ML estimation performance, then the method is 'suboptimal' or not good enough. I don't quite remember what is the meaning of ML estimation performance and sub-optimal or optimal. When is a method optimal and optimal with respect to what? I am following the book titled: "Statistical signal processing Vol 1 by S. Kay". As a beginner and self-learner it is quite hard to grasp the actual implication of the terms -- achieving ML estimation performance, optimal and sub-optimal. 

Can somebody please explain an intuitive meaning to these terms? Thank you.
There are 3 major reasons in my opinion which makes the Maximum Likelihood Estimator so popular:


Intuition
It is very clear what's the logic behind this method and what you maximize. It makes sense even if you have little knowledge in probability and statistics.
Properties
It has great properties (Some are only asymptotically, namely with many samples) such as consistency and efficiency. This means it always makes some sense to use it. 
Easy to Derive
It is usually pretty easy to derive the optimization problem defined by the MLE and in some cases it is also simple to solve it.


Now, many parameter estimation methods are compared to the properties of the MLE - Efficiency and Consistency.
If they exhibit similar performance than they are as optimal as the MLE.
Otherwise, they are not, as expected.