Q:

Is there a Spark built-in that flattens nested arrays?

I have a DataFrame field that is a Seq[Seq[String]] I built a UDF to transform said column into a column of Seq[String]; basically, a UDF for the flatten function from Scala. 
def combineSentences(inCol: String, outCol: String): DataFrame => DataFrame = {

    def flatfunc(seqOfSeq: Seq[Seq[String]]): Seq[String] = seqOfSeq match {
        case null => Seq.empty[String]
        case _ => seqOfSeq.flatten
    }
    df: DataFrame => df.withColumn(outCol, udf(flatfunc _).apply(col(inCol)))
}

My use case is strings, but obviously, this could be generic. You can use this function in a chain of DataFrame transforms like:
df.transform(combineSentences(inCol, outCol))

Is there a Spark built-in function that does the same thing? I have not been able to find one. 

A:

There is a similar function (since Spark 2.4) and it is called flatten:
import org.apache.spark.sql.functions.flatten

From the official documentation:

def flatten(e: Column): Column
Creates a single array from an array of arrays. If a structure of nested arrays is deeper than two levels, only one level of nesting is removed.
Since
2.4.0

To get the exact equivalent you'll have to coalesce to replace NULL.

