They're sometimes a good technique only because some problems are really simple. There are almost no problems where the extreme independence assumptions of naive Bayes create a reasonable likelihood function. The consequence ends up that when it's wrong, it tends to be very very certain that it's right. I think the aphorism that gets passed around is "Naive Bayes classifiers are often in error but never uncertain".

Yup. But some problems -- for instance, discriminating between spam and non-spam emails, and keeping up decent discrimination as spammers vary their tactics -- are (1) "really simple" in that sense and (2) apparently quite difficult to solve, given that there basically were no really effective spam filters before naive-Bayes ones came along.