I would personally be very surprised if machine learning was of any use in a known plaintext attack.
We design our ciphers to look a lot like random functions; you give the black box an input, and an output spits out. You give it a second input (possibly the same input in the case of nondetermanistic encryption), and a second output spits out. What we try to achieve is that no one can determine whether the black box was our cipher (with an unknown key), or whether it's just spitting out random outputs.
Now, we assume that the attacker has the complete design of our input (apart from the 'key'); in a successful cipher, he still cannot determine it. In fact, we design things so that the attacker can submit inputs of his own choosing; he still cannot determine whether he's giving inputs to the cipher or a random function.
Now, what machine learning would be trying to do is essentially this, except that you would be ignoring the design (because there's no way to give the design to the machine learning process), and you are limited to a known plaintext attack (because your machine learner has no way to generate the chosen inputs). In addition, your machine learner would also not have the best asset a cryptanalyst has (which is his cleverness; programs aren't good at 'clever').
Now, I don't know enough about machine learning to say that it can't be used elsewhere (or that a clever cryptanalysis couldn't use a machine learning program to help with his cryptanalysis); however it certainly sounds to me that machine learning would not be enough to do the entire cryptanalysis job.