Queries with (equality or LIKE) selection predicates over string attributes are widely used in relational databases. However, state-of-the-art techniques for estimating selectivities of string predicates are often biased towards severely underestimating selectivities. In this paper, we develop accurate selectivity estimators for string predicates that adapt to data and query characteristics, and which can exploit and build on a variety of existing estimators. A thorough experimental evaluation over real data sets demonstrates the resilience of our estimators to variations in both data and query characteristics.