The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods; (ii) high-frequency data increases the performance of estimation models; (iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data; (iv) the pandemic causes an adverse effect on the performance of estimation models; (v) energy-related variables are more important than other variables although all are significant; (vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed.