Minimum SNR Requirements for Maximum Likelihood Frequency Estimation In certain applications, you have enough SNR available to, for example, perform an FFT and identify peak location and hence the signal frequency. If my understanding is correct, parameter estimation techniques, such as maximum likelihood, will only be useful when your SNR is so low that you can't run a basic peak search to identify frequency. 

However, I have seen literature where people plot performance of various estimation algorithms vs SNRs upto 40~50dB. It seems like a waste of processing power to perform ML on a high SNR (40dB is quite a high value) signal and extract the already obvious information?

Is there a 'rule of thumb' to say, use statistical algorithms below such SNR levels?
I doubt that there is such a rule because your assumption- the negative of computational work more than offsets the benefits of lower error probabilities- assumes that computations are expensive and errors are not. The "costs" of computational work and errors depends entirely on the application.