Why Logistic regression into Spark Mllib does not use Maximum likelihood estimation? During comparison estimates/ coefficients in 'R'&amp; Spark Mllib of Logistic regression, It has been observed that estimates are not same. 

On further investigation, I found that R &amp; Mllib has different implementations for Logistic regression.


  R's glm is return a maximum likelihood estimate of the model while
  Spark's LogisticRegressionWithLBFGS is return a regularized model
  estimate.


Maximum likelihood estimates seems more efficient, as per literature available. 

I am curious to know, why Spark Mllib developers has not chosen 'Maximum likelihood estimation' technique?
I think you're confused. Maximum likelihood is a general technique for giving the most likely parameter estimation. There is no closed solution for maximum likelihood for logistic regression, so both R and Spark must numerically estimate it. 

How exactly the likelihood is estimated could be slightly different and might depends on the implementation. For example, Spark prefers to add regularization, R might like to use something else (you'll need to check the documentation).

Summary: Both R and Spark try to estimate ML for you. Please check the R documentation on how R do it.