Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these ratings are notoriously difficult to estimate with numerical methods, so that most are still developed manually. A few automated algorithms have emerged, but their use is sporadic, and their relative merits have not been rigorously assessed. One recently developed approach, the generalized power-law, avoids the segmenting problem by representing the power-law exponent as a Gaussian process. On the one hand, this representation is more flexible and easier to fit, but its flexibility might allow unrealistic solutions, so it needs to be tested under a range of conditions to assess its operational viability. This study evaluates the generalized power-law rating curve model by applying it to observations from 180 streams in Iceland, Sweden, and the United States. Overall, the model proved flexible and computationally robust, generating convincing rating curves across a range of geographic settings and was comparable to curves generated by a segmented rating model. Lastly, we propose a model-selection algorithm based on information theory to help identify the best rating curve model for a particular stream gauge.